640 citations · 1.4k across the 17 of their papers we have counts for
19 papers
Nonlinear Tensor Decomposition for Pattern Discovery
Atharva Awari, Geert Roelof van der Ploeg, Arnaud Vandaele +2
The CANDECOMP/PARAFAC (CP) decomposition is widely used for revealing the underlying patterns from multiway data (also referred to as a higher-order tensor). When the data is clipp…
Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations
Gaute Johannessen, Geert Roelof van der Ploeg, Evrim Acar
In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multi…
dCMF: Learning interpretable evolving patterns from temporal multiway data
Christos Chatzis, Carla Schenker, Jérémy E. Cohen +1
Multiway datasets are commonly analyzed using unsupervised matrix and tensor factorization methods to reveal underlying patterns. Frequently, such datasets include timestamps and c…
tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data
Christos Chatzis, Carla Schenker, Max Pfeffer +1
Tensor factorizations have been widely used for the task of uncovering patterns in various domains. Often, the input is time-evolving, shifting the goal to tracking the evolution o…
PARAFAC2-based Coupled Matrix and Tensor Factorizations with Constraints
Carla Schenker, Xiulin Wang, David Horner +2
Data fusion models based on Coupled Matrix and Tensor Factorizations (CMTF) have been effective tools for joint analysis of data from multiple sources. While the vast majority of C…
A Time-aware tensor decomposition for tracking evolving patterns
Christos Chatzis, Max Pfeffer, Pedro Lind +1
Time-evolving data sets can often be arranged as a higher-order tensor with one of the modes being the time mode. While tensor factorizations have been successfully used to capture…